In endeavors to instill trust in these systems, an abundance of explainable AI (XAI) techniques have been introduced. However, these proposed techniques not only approach similar but distinct facets of interpretability – they also associate notions of interpretability (and explanation) with different meanings. Several prevalent taxonomies have aimed to disentangle some of the perplexity of the language and concepts
[1]}, [2]}, [3]}.
Nonetheless, there is little consensus as to what interpretability is and, consequentially, how it may be evaluated. Existing efforts to assess the caliber of an XAI method rely on subjective or proxy means, which demonstrably mislead
or emphasize plausibility over fidelity [4]}, [2]}, [6]}.
Understanding the fidelity of XAI methods is especially important as they
are
applied to various high stakes domains within both research and industry,
such as medical diagnosis, credit risk analysis, and epidemiological modeling [7]}, [8]}, [9]}.
